Overview

Dataset statistics

Number of variables13
Number of observations1127650
Missing cells30
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory106.5 MiB
Average record size in memory99.0 B

Variable types

Categorical2
DateTime3
Numeric8

Alerts

ch_cpf_hash has a high cardinality: 225530 distinct valuesHigh cardinality
vl_min_limite_aprovado is highly overall correlated with vl_max_limite_aprovado and 1 other fieldsHigh correlation
vl_max_limite_aprovado is highly overall correlated with vl_min_limite_aprovado and 1 other fieldsHigh correlation
vl_total_limite_aprovado is highly overall correlated with vl_min_limite_aprovado and 2 other fieldsHigh correlation
min_saldo_devedor is highly overall correlated with max_saldo_devedor and 1 other fieldsHigh correlation
max_saldo_devedor is highly overall correlated with min_saldo_devedor and 1 other fieldsHigh correlation
total_saldo_devedor is highly overall correlated with min_saldo_devedor and 1 other fieldsHigh correlation
min_dias_atraso is highly overall correlated with max_dias_atrasoHigh correlation
max_dias_atraso is highly overall correlated with min_dias_atrasoHigh correlation
vl_qnt_conta is highly overall correlated with vl_total_limite_aprovadoHigh correlation
vl_qnt_conta is highly imbalanced (99.8%)Imbalance
ch_cpf_hash is uniformly distributedUniform
min_saldo_devedor has 167362 (14.8%) zerosZeros
max_saldo_devedor has 167331 (14.8%) zerosZeros
total_saldo_devedor has 167284 (14.8%) zerosZeros
min_dias_atraso has 767362 (68.0%) zerosZeros
max_dias_atraso has 767268 (68.0%) zerosZeros

Reproduction

Analysis started2024-11-22 20:46:06.910942
Analysis finished2024-11-22 20:46:43.825097
Duration36.91 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

ch_cpf_hash
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct225530
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
0da564d51ee1ce79bd60bd0b723238c3878aa1d54d6da4ae1619be8de3c5dde9
 
5
8b5702cc48fc721533b162ea7b3873d3701b61e0330f3df8b10bea93865cbd5c
 
5
008d214e8b13b0e92be2f88dd6433d2a8f21d98970ad57834fb6eacfd0a29df5
 
5
542a5598a7d9dcb5f81a5144dba67fed635593d7d2230b2c4c6550500b1c0d46
 
5
9326ccdb50621a8d89850305b3babdde6ba95b91f43edb08f828233e8d49af56
 
5
Other values (225525)
1127625 

Length

Max length64
Median length64
Mean length64
Min length64

Characters and Unicode

Total characters72169600
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0da564d51ee1ce79bd60bd0b723238c3878aa1d54d6da4ae1619be8de3c5dde9
2nd rowd2a444414ab222bf58b39090d7e64a66b2c1cdef2dad370a67b7b8b7d80297d5
3rd row39e26353f2a9de3b00f2ff1a50bcaaad04101251e794c9117f7506e0a170e5b0
4th rowb87362d224305c9c0f08fcc98df0e0703cb159b5bb2ea7d265b5d729678e0906
5th rowf9978d9d96ae87331838f3eff26f19d78df9a8d52853a29e2d471bda7a1f313b

Common Values

ValueCountFrequency (%)
0da564d51ee1ce79bd60bd0b723238c3878aa1d54d6da4ae1619be8de3c5dde9 5
 
< 0.1%
8b5702cc48fc721533b162ea7b3873d3701b61e0330f3df8b10bea93865cbd5c 5
 
< 0.1%
008d214e8b13b0e92be2f88dd6433d2a8f21d98970ad57834fb6eacfd0a29df5 5
 
< 0.1%
542a5598a7d9dcb5f81a5144dba67fed635593d7d2230b2c4c6550500b1c0d46 5
 
< 0.1%
9326ccdb50621a8d89850305b3babdde6ba95b91f43edb08f828233e8d49af56 5
 
< 0.1%
39e26353f2a9de3b00f2ff1a50bcaaad04101251e794c9117f7506e0a170e5b0 5
 
< 0.1%
b87362d224305c9c0f08fcc98df0e0703cb159b5bb2ea7d265b5d729678e0906 5
 
< 0.1%
f9978d9d96ae87331838f3eff26f19d78df9a8d52853a29e2d471bda7a1f313b 5
 
< 0.1%
bf653baaa25761c0b618b7ab8bb658e3e3530724be75b19cac47f4aeaa654265 5
 
< 0.1%
af4b161d2249ef8459e23d74bc0716c6970231d1a66bcd2f7f9b1867ed9982a0 5
 
< 0.1%
Other values (225520) 1127600
> 99.9%

Length

2024-11-22T21:46:43.865377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0da564d51ee1ce79bd60bd0b723238c3878aa1d54d6da4ae1619be8de3c5dde9 5
 
< 0.1%
fc0da3de1876b85db46e1ad94476b8536d40a2170ab633d04fb15f59e84d8bac 5
 
< 0.1%
3b38be82fb6949af2f7840549a296d82b5623b57b13383a085480d8c557c086c 5
 
< 0.1%
b42352efb354e34f16f827edb2c7dcc72db96c62789fc5508f9ccde02377b447 5
 
< 0.1%
28714477c047e7a8cef2cba2c4374bef7eb4fdecc1305138aee9d44b1f04ef16 5
 
< 0.1%
d994cf330a7f7e787d55773700e67e6967cf92816f50d792030e57880e932d9f 5
 
< 0.1%
738178a373196d191b15834a0c0d23c60db1db835bd5bb0ad42f3c041b9eabc0 5
 
< 0.1%
5e02e2406d3c5dcece6b8bf6a5792118b3b6b5dbe7d8f8a52e5281702f29724b 5
 
< 0.1%
3147f2869c59bf026ac22fc7be5ac3f8447a9d70c76973be9508449fb770588c 5
 
< 0.1%
9c422b6561491a8ce9762b19b5a3eec963b6909a77d270b4ec03b85fbbaa0896 5
 
< 0.1%
Other values (225520) 1127600
> 99.9%

Most occurring characters

ValueCountFrequency (%)
c 4519320
 
6.3%
4 4518455
 
6.3%
f 4517350
 
6.3%
0 4515915
 
6.3%
6 4513735
 
6.3%
1 4511860
 
6.3%
9 4511810
 
6.3%
5 4511550
 
6.3%
b 4510695
 
6.3%
8 4509760
 
6.2%
Other values (6) 27029150
37.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45111635
62.5%
Lowercase Letter 27057965
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 4518455
10.0%
0 4515915
10.0%
6 4513735
10.0%
1 4511860
10.0%
9 4511810
10.0%
5 4511550
10.0%
8 4509760
10.0%
2 4509665
10.0%
7 4505625
10.0%
3 4503260
10.0%
Lowercase Letter
ValueCountFrequency (%)
c 4519320
16.7%
f 4517350
16.7%
b 4510695
16.7%
d 4508015
16.7%
a 4501940
16.6%
e 4500645
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 45111635
62.5%
Latin 27057965
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 4518455
10.0%
0 4515915
10.0%
6 4513735
10.0%
1 4511860
10.0%
9 4511810
10.0%
5 4511550
10.0%
8 4509760
10.0%
2 4509665
10.0%
7 4505625
10.0%
3 4503260
10.0%
Latin
ValueCountFrequency (%)
c 4519320
16.7%
f 4517350
16.7%
b 4510695
16.7%
d 4508015
16.7%
a 4501940
16.6%
e 4500645
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72169600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 4519320
 
6.3%
4 4518455
 
6.3%
f 4517350
 
6.3%
0 4515915
 
6.3%
6 4513735
 
6.3%
1 4511860
 
6.3%
9 4511810
 
6.3%
5 4511550
 
6.3%
b 4510695
 
6.3%
8 4509760
 
6.2%
Other values (6) 27029150
37.5%

vl_qnt_conta
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
1
1127315 
2
 
330
54
 
5

Length

Max length2
Median length1
Mean length1.0000044
Min length1

Characters and Unicode

Total characters1127655
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1127315
> 99.9%
2 330
 
< 0.1%
54 5
 
< 0.1%

Length

2024-11-22T21:46:43.915347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-22T21:46:43.966275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1127315
> 99.9%
2 330
 
< 0.1%
54 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 1127315
> 99.9%
2 330
 
< 0.1%
5 5
 
< 0.1%
4 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1127655
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1127315
> 99.9%
2 330
 
< 0.1%
5 5
 
< 0.1%
4 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1127655
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1127315
> 99.9%
2 330
 
< 0.1%
5 5
 
< 0.1%
4 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1127655
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1127315
> 99.9%
2 330
 
< 0.1%
5 5
 
< 0.1%
4 5
 
< 0.1%
Distinct2106
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
Minimum2016-09-14 00:00:00
Maximum2024-04-05 00:00:00
2024-11-22T21:46:44.012991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:44.074845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2106
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
Minimum2016-09-14 00:00:00
Maximum2024-04-05 00:00:00
2024-11-22T21:46:44.133849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:44.195506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

vl_min_limite_aprovado
Real number (ℝ)

Distinct229
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2124.7229
Minimum0
Maximum12500
Zeros45
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2024-11-22T21:46:44.256403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile600
Q11100
median1800
Q32600
95-th percentile5000
Maximum12500
Range12500
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation1622.0309
Coefficient of variation (CV)0.76340821
Kurtosis11.444531
Mean2124.7229
Median Absolute Deviation (MAD)700
Skewness2.7459947
Sum2.3959225 × 109
Variance2630984.3
MonotonicityNot monotonic
2024-11-22T21:46:44.319796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 85125
 
7.5%
2000 54735
 
4.9%
1300 54405
 
4.8%
1500 51160
 
4.5%
1000 44965
 
4.0%
1700 43015
 
3.8%
1200 41995
 
3.7%
1800 41835
 
3.7%
1400 40530
 
3.6%
1100 39355
 
3.5%
Other values (219) 630520
55.9%
ValueCountFrequency (%)
0 45
 
< 0.1%
100 15
 
< 0.1%
300 5
 
< 0.1%
500 39035
3.5%
600 85125
7.5%
700 29610
 
2.6%
727.21 5
 
< 0.1%
800 25515
 
2.3%
841.42 5
 
< 0.1%
900 31705
 
2.8%
ValueCountFrequency (%)
12500 4900
0.4%
12400 75
 
< 0.1%
12300 85
 
< 0.1%
12200 65
 
< 0.1%
12100 60
 
< 0.1%
12000 410
 
< 0.1%
11900 135
 
< 0.1%
11800 115
 
< 0.1%
11700 75
 
< 0.1%
11600 130
 
< 0.1%

vl_max_limite_aprovado
Real number (ℝ)

Distinct227
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2125.1251
Minimum0
Maximum12500
Zeros45
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2024-11-22T21:46:44.381286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile600
Q11100
median1800
Q32600
95-th percentile5000
Maximum12500
Range12500
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation1622.3318
Coefficient of variation (CV)0.76340533
Kurtosis11.440301
Mean2125.1251
Median Absolute Deviation (MAD)700
Skewness2.7455514
Sum2.3963761 × 109
Variance2631960.6
MonotonicityNot monotonic
2024-11-22T21:46:44.445496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 85100
 
7.5%
2000 54735
 
4.9%
1300 54400
 
4.8%
1500 51155
 
4.5%
1000 44965
 
4.0%
1700 43020
 
3.8%
1200 41990
 
3.7%
1800 41820
 
3.7%
1400 40515
 
3.6%
1100 39345
 
3.5%
Other values (217) 630595
55.9%
ValueCountFrequency (%)
0 45
 
< 0.1%
100 15
 
< 0.1%
300 5
 
< 0.1%
500 39020
3.5%
600 85100
7.5%
700 29595
 
2.6%
727.21 5
 
< 0.1%
800 25505
 
2.3%
841.42 5
 
< 0.1%
900 31700
 
2.8%
ValueCountFrequency (%)
12500 4905
0.4%
12400 75
 
< 0.1%
12300 85
 
< 0.1%
12200 65
 
< 0.1%
12100 60
 
< 0.1%
12000 410
 
< 0.1%
11900 135
 
< 0.1%
11800 115
 
< 0.1%
11700 75
 
< 0.1%
11600 130
 
< 0.1%

vl_total_limite_aprovado
Real number (ℝ)

Distinct238
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2126.6723
Minimum0
Maximum96700
Zeros45
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size8.6 MiB
2024-11-22T21:46:44.509903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile600
Q11100
median1800
Q32600
95-th percentile5000
Maximum96700
Range96700
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation1636.7232
Coefficient of variation (CV)0.76961703
Kurtosis60.529729
Mean2126.6723
Median Absolute Deviation (MAD)700
Skewness3.5454227
Sum2.3981207 × 109
Variance2678862.9
MonotonicityNot monotonic
2024-11-22T21:46:44.571347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 85080
 
7.5%
2000 54737
 
4.9%
1300 54386
 
4.8%
1500 51138
 
4.5%
1000 44947
 
4.0%
1700 42994
 
3.8%
1200 41995
 
3.7%
1800 41796
 
3.7%
1400 40503
 
3.6%
1100 39338
 
3.5%
Other values (228) 630726
55.9%
ValueCountFrequency (%)
0 45
 
< 0.1%
100 15
 
< 0.1%
300 5
 
< 0.1%
500 39003
3.5%
600 85080
7.5%
700 29593
 
2.6%
727.21 5
 
< 0.1%
800 25495
 
2.3%
841.42 5
 
< 0.1%
900 31696
 
2.8%
ValueCountFrequency (%)
96700 5
 
< 0.1%
25000 2
 
< 0.1%
22000 1
 
< 0.1%
17000 1
 
< 0.1%
16500 5
 
< 0.1%
16400 2
 
< 0.1%
15300 5
 
< 0.1%
14400 2
 
< 0.1%
13000 1
 
< 0.1%
12500 4898
0.4%

min_saldo_devedor
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct295712
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1270.6353
Minimum-12464.4
Maximum56578.72
Zeros167362
Zeros (%)14.8%
Negative164630
Negative (%)14.6%
Memory size8.6 MiB
2024-11-22T21:46:44.632584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-12464.4
5-th percentile-11.7955
Q10
median210
Q31501.36
95-th percentile6017.18
Maximum56578.72
Range69043.12
Interquartile range (IQR)1501.36

Descriptive statistics

Standard deviation2404.5248
Coefficient of variation (CV)1.89238
Kurtosis23.522805
Mean1270.6353
Median Absolute Deviation (MAD)219.38
Skewness3.7005457
Sum1.4328319 × 109
Variance5781739.5
MonotonicityNot monotonic
2024-11-22T21:46:44.689991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 167362
 
14.8%
13.9 8993
 
0.8%
0.02 7502
 
0.7%
0.04 5345
 
0.5%
0.06 5061
 
0.4%
0.03 4402
 
0.4%
0.01 4119
 
0.4%
0.05 3882
 
0.3%
0.07 2942
 
0.3%
-0.01 2899
 
0.3%
Other values (295702) 915143
81.2%
ValueCountFrequency (%)
-12464.4 5
< 0.1%
-8303.4 1
 
< 0.1%
-8300.36 1
 
< 0.1%
-7842.1 1
 
< 0.1%
-7826.91 5
< 0.1%
-7380.8 1
 
< 0.1%
-6919.5 1
 
< 0.1%
-6481.42 1
 
< 0.1%
-6458.2 1
 
< 0.1%
-5939.45 5
< 0.1%
ValueCountFrequency (%)
56578.72 1
 
< 0.1%
55849.74 5
< 0.1%
55399.84 4
< 0.1%
50193.99 5
< 0.1%
46176.53 5
< 0.1%
46121.41 5
< 0.1%
44642.1 1
 
< 0.1%
44320.22 1
 
< 0.1%
43644.07 1
 
< 0.1%
43553.86 1
 
< 0.1%

max_saldo_devedor
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct295764
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1271.0835
Minimum-12464.4
Maximum56578.72
Zeros167331
Zeros (%)14.8%
Negative164429
Negative (%)14.6%
Memory size8.6 MiB
2024-11-22T21:46:44.754441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-12464.4
5-th percentile-11.76
Q10
median210.565
Q31502.1575
95-th percentile6018.222
Maximum56578.72
Range69043.12
Interquartile range (IQR)1502.1575

Descriptive statistics

Standard deviation2404.7405
Coefficient of variation (CV)1.8918824
Kurtosis23.51215
Mean1271.0835
Median Absolute Deviation (MAD)219.875
Skewness3.6995788
Sum1.4333373 × 109
Variance5782776.6
MonotonicityNot monotonic
2024-11-22T21:46:44.813331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 167331
 
14.8%
13.9 8992
 
0.8%
0.02 7492
 
0.7%
0.04 5350
 
0.5%
0.06 5061
 
0.4%
0.03 4402
 
0.4%
0.01 4124
 
0.4%
0.05 3887
 
0.3%
0.07 2947
 
0.3%
-0.01 2888
 
0.3%
Other values (295754) 915176
81.2%
ValueCountFrequency (%)
-12464.4 5
< 0.1%
-8303.4 1
 
< 0.1%
-8300.36 1
 
< 0.1%
-7842.1 1
 
< 0.1%
-7826.91 5
< 0.1%
-7380.8 1
 
< 0.1%
-6919.5 1
 
< 0.1%
-6481.42 1
 
< 0.1%
-6458.2 1
 
< 0.1%
-5939.45 5
< 0.1%
ValueCountFrequency (%)
56578.72 1
 
< 0.1%
55849.74 5
< 0.1%
55399.84 4
< 0.1%
50193.99 5
< 0.1%
46176.53 5
< 0.1%
46121.41 5
< 0.1%
44642.1 1
 
< 0.1%
44320.22 1
 
< 0.1%
43644.07 1
 
< 0.1%
43553.86 1
 
< 0.1%

total_saldo_devedor
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct295802
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1271.4184
Minimum-12464.4
Maximum56578.72
Zeros167284
Zeros (%)14.8%
Negative164522
Negative (%)14.6%
Memory size8.6 MiB
2024-11-22T21:46:45.012844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-12464.4
5-th percentile-11.78
Q10
median210.57
Q31502.45
95-th percentile6019.722
Maximum56578.72
Range69043.12
Interquartile range (IQR)1502.45

Descriptive statistics

Standard deviation2405.4416
Coefficient of variation (CV)1.8919355
Kurtosis23.495039
Mean1271.4184
Median Absolute Deviation (MAD)219.91
Skewness3.6988423
Sum1.4337149 × 109
Variance5786149.3
MonotonicityNot monotonic
2024-11-22T21:46:45.073135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 167284
 
14.8%
13.9 8991
 
0.8%
0.02 7492
 
0.7%
0.04 5345
 
0.5%
0.06 5061
 
0.4%
0.03 4402
 
0.4%
0.01 4119
 
0.4%
0.05 3877
 
0.3%
0.07 2942
 
0.3%
-0.01 2888
 
0.3%
Other values (295792) 915249
81.2%
ValueCountFrequency (%)
-12464.4 5
< 0.1%
-8303.4 1
 
< 0.1%
-8300.36 1
 
< 0.1%
-7842.1 1
 
< 0.1%
-7826.91 5
< 0.1%
-7380.8 1
 
< 0.1%
-6919.5 1
 
< 0.1%
-6481.42 1
 
< 0.1%
-6458.2 1
 
< 0.1%
-5939.45 5
< 0.1%
ValueCountFrequency (%)
56578.72 1
 
< 0.1%
55849.74 5
< 0.1%
55399.84 4
< 0.1%
50193.99 5
< 0.1%
46176.53 5
< 0.1%
46121.41 5
< 0.1%
44642.1 1
 
< 0.1%
44320.22 1
 
< 0.1%
43644.07 1
 
< 0.1%
43553.86 1
 
< 0.1%

min_dias_atraso
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1067
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.92519
Minimum0
Maximum2762
Zeros767362
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-11-22T21:46:45.134506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3144
95-th percentile571
Maximum2762
Range2762
Interquartile range (IQR)144

Descriptive statistics

Standard deviation232.5313
Coefficient of variation (CV)2.2161628
Kurtosis22.489317
Mean104.92519
Median Absolute Deviation (MAD)0
Skewness3.9059835
Sum1.1831889 × 108
Variance54070.806
MonotonicityNot monotonic
2024-11-22T21:46:45.188823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 767362
68.0%
296 8004
 
0.7%
7 7555
 
0.7%
265 7176
 
0.6%
281 7041
 
0.6%
250 6592
 
0.6%
6 5944
 
0.5%
291 5642
 
0.5%
189 5399
 
0.5%
204 5312
 
0.5%
Other values (1057) 301623
 
26.7%
ValueCountFrequency (%)
0 767362
68.0%
6 5944
 
0.5%
7 7555
 
0.7%
11 1836
 
0.2%
12 2557
 
0.2%
16 1856
 
0.2%
17 2334
 
0.2%
20 1
 
< 0.1%
21 1678
 
0.1%
22 2116
 
0.2%
ValueCountFrequency (%)
2762 1
 
< 0.1%
2747 1
 
< 0.1%
2731 1
 
< 0.1%
2717 1
 
< 0.1%
2716 1
 
< 0.1%
2710 1
 
< 0.1%
2706 1
 
< 0.1%
2701 3
< 0.1%
2686 4
< 0.1%
2679 1
 
< 0.1%

max_dias_atraso
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1067
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.02991
Minimum0
Maximum2762
Zeros767268
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size5.4 MiB
2024-11-22T21:46:45.246930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3144
95-th percentile574
Maximum2762
Range2762
Interquartile range (IQR)144

Descriptive statistics

Standard deviation232.89859
Coefficient of variation (CV)2.21745
Kurtosis22.543779
Mean105.02991
Median Absolute Deviation (MAD)0
Skewness3.912082
Sum1.1843698 × 108
Variance54241.751
MonotonicityNot monotonic
2024-11-22T21:46:45.300617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 767268
68.0%
296 8005
 
0.7%
7 7555
 
0.7%
265 7177
 
0.6%
281 7042
 
0.6%
250 6593
 
0.6%
6 5956
 
0.5%
291 5643
 
0.5%
189 5399
 
0.5%
204 5312
 
0.5%
Other values (1057) 301700
 
26.8%
ValueCountFrequency (%)
0 767268
68.0%
6 5956
 
0.5%
7 7555
 
0.7%
11 1841
 
0.2%
12 2558
 
0.2%
16 1857
 
0.2%
17 2334
 
0.2%
20 1
 
< 0.1%
21 1678
 
0.1%
22 2113
 
0.2%
ValueCountFrequency (%)
2762 1
 
< 0.1%
2747 1
 
< 0.1%
2731 1
 
< 0.1%
2717 1
 
< 0.1%
2716 1
 
< 0.1%
2710 1
 
< 0.1%
2706 1
 
< 0.1%
2701 3
< 0.1%
2686 4
< 0.1%
2679 1
 
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
Minimum2024-06-30 00:00:00
Maximum2024-10-31 00:00:00
2024-11-22T21:46:45.347990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:45.390368image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

Interactions

2024-11-22T21:46:40.408167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:32.554447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:33.720608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:34.779587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:35.803290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:36.805396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:37.811577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:38.873134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:40.569677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:32.676744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:33.838059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:34.895218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:35.919415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:36.920903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:37.929025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:39.080401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:40.744614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:32.898106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:33.958782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:35.013174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:36.036909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:37.039457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:38.046690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:39.243767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:40.901989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:33.019286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:34.079743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:35.127211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:36.140398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:37.148164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:38.154812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:39.394918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:41.065150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:33.143275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:34.202715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:35.245949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:36.252177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:37.259427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:38.271862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:39.555064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:41.226571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:33.269053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:34.327219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:35.364164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:36.367995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:37.369924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:38.383614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:39.715787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:41.437453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:33.437825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:34.499062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:35.530026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:36.529077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:37.533275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:38.546881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:40.038492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:41.639916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:33.602516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:34.664183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:35.689969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:36.687043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:37.692888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:38.706061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-11-22T21:46:40.243376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-11-22T21:46:45.433563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
vl_min_limite_aprovadovl_max_limite_aprovadovl_total_limite_aprovadomin_saldo_devedormax_saldo_devedortotal_saldo_devedormin_dias_atrasomax_dias_atrasovl_qnt_conta
vl_min_limite_aprovado1.0001.0000.9990.0310.0310.031-0.044-0.0440.003
vl_max_limite_aprovado1.0001.0001.0000.0300.0310.031-0.044-0.0440.013
vl_total_limite_aprovado0.9991.0001.0000.0300.0310.031-0.044-0.0440.707
min_saldo_devedor0.0310.0300.0301.0001.0001.0000.1760.1760.007
max_saldo_devedor0.0310.0310.0311.0001.0001.0000.1760.1760.003
total_saldo_devedor0.0310.0310.0311.0001.0001.0000.1760.1760.003
min_dias_atraso-0.044-0.044-0.0440.1760.1760.1761.0001.0000.003
max_dias_atraso-0.044-0.044-0.0440.1760.1760.1761.0001.0000.040
vl_qnt_conta0.0030.0130.7070.0070.0030.0030.0030.0401.000

Missing values

2024-11-22T21:46:41.752434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-22T21:46:42.331903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-22T21:46:43.288582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ch_cpf_hashvl_qnt_contadt_min_adesaodt_max_adesaovl_min_limite_aprovadovl_max_limite_aprovadovl_total_limite_aprovadomin_saldo_devedormax_saldo_devedortotal_saldo_devedormin_dias_atrasomax_dias_atrasodt_fechamento
00da564d51ee1ce79bd60bd0b723238c3878aa1d54d6da4ae1619be8de3c5dde912021-12-272021-12-27600.0600.0600.0307.78307.78307.781491492024-08-31
1d2a444414ab222bf58b39090d7e64a66b2c1cdef2dad370a67b7b8b7d80297d512021-12-282021-12-281900.01900.01900.02592.252592.252592.252202202024-08-31
239e26353f2a9de3b00f2ff1a50bcaaad04101251e794c9117f7506e0a170e5b012022-01-062022-01-061800.01800.01800.00.000.000.00002024-08-31
3b87362d224305c9c0f08fcc98df0e0703cb159b5bb2ea7d265b5d729678e090612022-01-132022-01-13700.0700.0700.0653.46653.46653.461141142024-08-31
4f9978d9d96ae87331838f3eff26f19d78df9a8d52853a29e2d471bda7a1f313b12022-01-132022-01-136100.06100.06100.083.4083.4083.4038382024-08-31
5bf653baaa25761c0b618b7ab8bb658e3e3530724be75b19cac47f4aeaa65426512022-01-192022-01-191800.01800.01800.00.010.010.01002024-08-31
6af4b161d2249ef8459e23d74bc0716c6970231d1a66bcd2f7f9b1867ed9982a012022-01-272022-01-271800.01800.01800.01313.071313.071313.071091092024-08-31
73d79d5e702a97715c8ae7156867aaa2dcb9d39cf512874981b066d7216812e1212022-02-032022-02-032400.02400.02400.0-17.14-17.14-17.14002024-08-31
85d1bbd70f4faec47499608dd2ae1544b416672f1ccdc6c870142cbdbcc07f5b312022-02-042022-02-043000.03000.03000.0-2.99-2.99-2.99002024-08-31
978f0dafad3f47eab57095e6fa19ef2ff3e0c30f95d8fe2c6cd4defc9012d79d912022-02-072022-02-074100.04100.04100.00.080.080.08002024-08-31
ch_cpf_hashvl_qnt_contadt_min_adesaodt_max_adesaovl_min_limite_aprovadovl_max_limite_aprovadovl_total_limite_aprovadomin_saldo_devedormax_saldo_devedortotal_saldo_devedormin_dias_atrasomax_dias_atrasodt_fechamento
1127640bc45f21a532d04cfb55eb92a9a2366c467d5a78fc806480de2cabab927278f2812022-01-172022-01-175400.05400.05400.099.1199.1199.1137372024-07-31
112764154bada5ba31ec97f93c061b77c32a2bff3d986f22efb52a26461e456b6336c1612022-01-272022-01-271900.01900.01900.0847.64847.64847.641891892024-07-31
1127642adb5756d13f8636e059d51693634b787f8246261767b31757001d3a31de1ac9012022-01-272022-01-271400.01400.01400.0337.93337.93337.93002024-07-31
11276433d114dbdb742103cf82c7379a77214f15a31ceb28a76a9fb49c10eff7bba668012021-11-192021-11-191200.01200.01200.0-1.74-1.74-1.74002024-07-31
1127644c2d6d6b9962c3f93e6190a120149e29fb7c19f8e6c90a3cbc534cc52a223fc0c12021-11-232021-11-23500.0500.0500.00.000.000.005955952024-07-31
1127645cf70e6f942351f8437d5b7dd55f08ab5106c9eb99bbf83b895059b3b62ff78b312021-11-242021-11-243200.03200.03200.03851.283851.283851.282602602024-07-31
1127646a9d75e1fa655232405f3ca772ba868400fa901d8c5f4a988eaf98b1622ed120512021-11-302021-11-30700.0700.0700.00.000.000.00002024-07-31
1127647f315412b769ed0ff8eeda6fb9cb26317e79eb9f74600edde8ce33590fc5dc89b12021-12-012021-12-011900.01900.01900.01.571.571.57002024-07-31
112764892dbec62fa1b3744a141d528d5c0d9d1765c44896f4fc4e06887fcc0749097b512021-12-012021-12-015900.05900.05900.01290.631290.631290.631731732024-07-31
1127649b38c98651c8a327de2997c561d38d5d1a95c130a7273e1e7ed3d606c1fbb17db12021-12-162021-12-16600.0600.0600.00.000.000.007537532024-07-31